The prevalence of smartphone and wearable device technologies has fueled an industry of health and fitness monitoring based on tracking biomechanical movements. Typically, motion sensors embedded in smartphones and/or wearable devices may be used to track relatively large movements, such as walking, running, biking, and the like. However, using motion sensors embedded in smartphones and/or wearable devices to track relatively small movements, such as arm, wrist, and/or hand movements, has proven to be more difficult.
Conventional techniques for tracking biomechanical movements use either computer vision (e.g., stereo, depth, and infrared cameras) or external localization systems (e.g., ultra-wideband (UWB) localization system, range radar, etc.). However, these systems usually require extensive hardware setups, and are typically only able to track movements within a predefined coverage area supported by the hardware setup. Additionally, these systems often suffer performance degradation from occlusion caused by the human body, occlusion caused by other objects, and/or environmental interference.
Some techniques for tracking biomechanical movements using motion sensors include applying various machine learning techniques to sensor data to identify certain activities, such as walking, running, cycling, etc. However, these machine learning techniques only identify a coarse semantic meaning of a particular activity, while a precise location trajectory of particular bodies/limbs involved in the identified activities is not known. Other techniques for tracking biomechanical movements using motion sensors generally require a plethora of motion sensors to be attached to multiple joints of a user. For example, many of these techniques require at least 7 inertial/magnetic sensors to track relative upper body motion. Using such a plethora of motion sensors may prove cumbersome and inhibit natural biomechanical movements.